Housing In Canada
Graphs of housing data in Canada using STATCAN data
Data
STATCAN Table: 18-10-0205-01
STATCAN Table: 34-10-0162-01
STATCAN Table: 36-10-0688-01
Prepare Data
# devtools::install_github("derekmichaelwright/agData")
library(agData)# Prep data
myCaption <- "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: STATCAN"
myColors_M <- c("purple4", "darkgreen", "darkorange")
myColors_P <- c("steelblue","darkred","darkblue")
#
d1 <- read.csv("1810020501_databaseLoadingData.csv") %>%
select(Date=1, Area=GEO, Measurement=4, Unit=UOM, Value=VALUE) %>%
separate(Date, c("Year", "Month"), sep = "-", remove = F) %>%
mutate(Date = as.Date(paste0(Date,"-01"), format = "%Y-%m-%d"))
pp <- data.frame(Admin = factor(1:4),
Party = factor(c("PC", "LIB", "CPC", "LIB"), levels = c("PC", "LIB", "CPC")),
xmin = as.Date(c("1984-09-17", "1993-11-04", "2006-02-06", "2015-11-04")),
xmax = as.Date(c("1993-11-04", "2006-02-06", "2015-11-04", "2022-06-01")))
#
myAreas <- c("Census metropolitan areas and census agglomerations of 50,000 and over",
"Census metropolitan areas", "Census agglomerations 50,000 and over")
d2 <- read.csv("3410016201_databaseLoadingData.csv") %>%
select(Date=REF_DATE, Area=GEO, Measurement=4, Unit=UOM, Value=VALUE) %>%
arrange(Area) %>%
mutate(Date = as.Date(paste0(Date,"-01"), format = "%Y-%m-%d"),
Year = as.numeric(substr(Date, 1, 4)),
Area = factor(Area, levels = unique(c(myAreas, .$Area))))
#
myProvs <- c("Canada", "British Columbia", "Alberta", "Saskatchewan",
"Manitoba", "Ontario", "Quebec",
"Prince Edward Island", "New Brunswick",
"Nova Scotia", "Newfoundland and Labrador",
"Yukon", "Northwest Territories", "Nunavut")
myTypes <- c("Total dwelling type", "Single house", "Double house",
"Row house", "Apartment building", "Mobile house")
d3 <- read.csv("3610068801_databaseLoadingData.csv") %>%
select(Date=REF_DATE, Area=GEO, Sector=4, Private=5, Occupancy=6, Type=7, Tenure=8,
Unit=UOM, Value=VALUE) %>%
mutate(Date = as.Date(paste0(Date,"-01"), format = "%Y-%m-%d"),
Year = substr(Date, 1, 4),
Type = factor(Type, levels = myTypes),
Area = factor(Area, levels = myProvs))House Prices
Canada
# Prep data
xx <- d1 %>%
filter(Area == "Canada", Measurement %in% c("House only", "Land only"))
myxmin <- min(xx$Value)
myxmax <- max(xx$Value)
# Plot
mp <- ggplot(xx) +
geom_rect(data = pp, alpha = 0.3, ymin = -Inf, ymax = Inf,
aes(xmin = xmin, xmax = xmax, fill = Party)) +
geom_line(aes(x = Date, y = Value, color = Measurement), alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myColors_M) +
scale_fill_manual(name = NULL, values = myColors_P) +
scale_y_continuous(limits = c(myxmin,myxmax)) +
theme_agData(legend.position = "bottom") +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_01.png", mp, width = 6, height = 4)Percent Change
# Prep data
xx <- d1 %>% filter(Area == "Canada", Measurement == "Total (house and land)")
for(i in 1:nrow(pp)) {
pp$price1[i] <- xx$Value[xx$Date == paste0(substr(pp$xmin[i],1,7),"-01")]
pp$price2[i] <- xx$Value[xx$Date == paste0(substr(pp$xmax[i],1,7),"-01")]
pp$PercentChange[i] <- (100 * pp$price2[i] / pp$price1[i]) - 100
}
# Plot
mp <- ggplot(pp, aes(x = Admin, y = PercentChange, fill = Party)) +
geom_col(color = "black", alpha = 0.7) +
scale_x_discrete(labels = paste(pp$xmin, pp$xmax, sep = "\n")) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Canada - Change in House Prices", x = NULL,
y = "Percent Change", caption = myCaption)
ggsave("canada_housing_1_02.png", mp, width = 5, height = 4)All Data
# Prep data
xx <- d1 %>% filter(Measurement == "Total (house and land)")
# Plot
mp <- ggplot(xx) +
geom_rect(data = pp, alpha = 0.3, ymin = -Inf, ymax = Inf,
aes(xmin = xmin, xmax = xmax, fill = Party)) +
geom_line(aes(x = Date, y = Value), size = 1.5, alpha = 0.7) +
facet_wrap(Area ~ ., ncol = 5) +
scale_color_manual(name = NULL, values = myColors_M) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_03.png", mp, width = 16, height = 16)Saskatchewan
# Prep data
xx <- d1 %>%
filter(grepl("Saskatchewan", Area),
Measurement %in% c("House only", "Land only"))
# Plot
mp <- ggplot(xx) +
geom_rect(data = pp, alpha = 0.3, ymin = -Inf, ymax = Inf,
aes(xmin = xmin, xmax = xmax, fill = Party)) +
geom_line(aes(x = Date, y = Value, color = Measurement),
alpha = 0.7, size = 1.5) +
facet_grid(. ~ Area) +
scale_color_manual(name = NULL, values = myColors_M) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Saskatchewan", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_04.png", mp, width = 8, height = 4)Percent Change
# Prep data
xx <- d1 %>% filter(Area == "Regina, Saskatchewan",
Measurement == "Total (house and land)")
for(i in 1:nrow(pp)) {
pp$price1[i] <- xx$Value[xx$Date == paste0(substr(pp$xmin[i],1,7),"-01")]
pp$price2[i] <- xx$Value[xx$Date == paste0(substr(pp$xmax[i],1,7),"-01")]
pp$PercentChange[i] <- (100 * pp$price2[i] / pp$price1[i]) - 100
}
# Plot
mp <- ggplot(pp, aes(x = Admin, y = PercentChange, fill = Party)) +
geom_col(color = "black", alpha = 0.7) +
scale_x_discrete(labels = paste(pp$xmin, pp$xmax, sep = "\n")) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Regina, Saskatchewan - Change in House Prices",
y = "Percent Change", x = NULL, caption = myCaption)
ggsave("canada_housing_1_05.png", mp, width = 5, height = 4)Regions
# Prep data
myAs <- c("Canada", "Ontario", "Quebec", "Prairie Region")
myCs <- c("darkred", "darkblue", "steelblue", "darkgreen")
xx <- d1 %>%
filter(Measurement == "Total (house and land)",
Area %in% myAs, Year > 2016) %>%
mutate(Area = factor(Area, levels = myAs))
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Area)) +
geom_line(alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myCs) +
scale_x_date(date_breaks = "year", date_labels = "%Y") +
theme_agData(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_06.png", mp, width = 6, height = 4)# Prep data
myAs <- c("Canada", "Ontario", "Quebec", "Prairie Region")
myCs <- c("darkred", "darkblue", "steelblue", "darkgreen")
xx <- d1 %>%
filter(Measurement == "Total (house and land)",
Area %in% myAs) %>%
mutate(Area = factor(Area, levels = myAs))
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Area)) +
geom_line(alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myCs) +
scale_x_date(date_breaks = "10 years", date_labels = "%Y",
date_minor_breaks = "1 year") +
theme_agData(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_07.png", mp, width = 6, height = 4)Home Builds
Canada
# Prep data
xx <- d2 %>%
filter(Area %in% myAreas[1], Measurement == "Total units") %>%
group_by(Year, Area) %>%
summarise(Value = mean(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value)) +
geom_col(position = "dodge", color = "black",
alpha = 0.7, fill = "darkgreen") +
scale_x_continuous(breaks = 1992:2022) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_01.png", mp, width = 7, height = 5)# Prep data
xx <- d2 %>%
filter(Area %in% myAreas[1], Measurement != "Total units") %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = mean(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Measurement)) +
geom_col(position = "dodge", color = "black", alpha = 0.7) +
scale_fill_manual(name = NULL, values = myColors_P) +
scale_x_continuous(breaks = 1992:2022) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_02.png", mp, width = 6, height = 4)# Prep data
xx <- xx %>% filter(Year >= 2005)
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Measurement)) +
geom_col(position = "dodge", color = "black", alpha = 0.7) +
scale_fill_manual(name = NULL, values = myColors_P) +
scale_x_continuous(breaks = 2005:2022) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_03.png", mp, width = 6, height = 4)# Prep data
xx <- xx %>% filter(Year >= 2019)
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Measurement)) +
geom_col(position = "dodge", color = "black", alpha = 0.7) +
scale_fill_manual(name = NULL, values = myColors_P) +
scale_x_continuous(breaks = 2005:2022) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_04.png", mp, width = 5, height = 4)Cities
# Prep data
xx <- d2 %>%
filter(Area != myAreas[1], Measurement != "Total units") %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = mean(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Measurement)) +
geom_col(position = "dodge", alpha = 0.7) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 6) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Housing Units Built in Canada",
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_05.png", mp, width = 20, height = 15)# Prep data
xx <- d2 %>%
filter(Area != myAreas[1], Measurement != "Total units")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Measurement)) +
geom_line(alpha = 0.7) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 6) +
scale_color_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Housing Units Built in Canada",
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_06.png", mp, width = 20, height = 15)Van + Tor + Cal + Reg
# Prep data
myCities <- c("Vancouver, British Columbia", "Toronto, Ontario",
"Calgary, Alberta", "Regina, Saskatchewan")
xx <- d2 %>%
filter(Area %in% myCities, Measurement != "Total units") %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = mean(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Measurement)) +
geom_col(position = "dodge", color = "black", alpha = 0.7, lwd = 0.2) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 2) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(subtitle = "Housing Units Built in Canada",
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_07.png", mp, width = 8, height = 6)Occupancy
# Prep data
xx <- d3 %>%
filter(Area == "Canada", Sector == "Total economy",
Private == "Total housing type", Tenure == "Total tenure type",
Occupancy == "Private dwellings unoccupied")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000)) +
#geom_line(alpha = 0.7, size = 1.5, color = "darkred") +
geom_col(alpha = 0.7, fill = "steelblue") +
facet_wrap(Type ~ ., scales = "free_y") +
theme_agData() +
labs(title = "Unoccupied Housing Units in Canada",
y = "Thousand Units", x = NULL, caption = myCaption)
ggsave("canada_housing_3_01.png", mp, width = 8, height = 6)# Prep data
xx <- d3 %>%
filter(Area != "Canada", Sector == "Total economy", Type == "Total dwelling type",
Private == "Total housing type", Tenure == "Total tenure type",
Occupancy == "Private dwellings unoccupied")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000)) +
#geom_line(alpha = 0.7, size = 1.5, color = "darkred") +
geom_col(alpha = 0.7, fill = "steelblue") +
facet_wrap(Area ~ ., scales = "free_y") +
theme_agData() +
labs(title = "Unoccupied Housing Units in Canada",
y = "Thousand Units", x = NULL, caption = myCaption)
ggsave("canada_housing_3_02.png", mp, width = 12, height = 8)Tenure
# Prep data
xx <- d3 %>%
filter(Area == "Canada", Sector == "Total economy", Type == "Total dwelling type",
Private == "Total housing type", Tenure %in% c("Owner", "Renter"),
Occupancy == "Total private dwellings") %>%
mutate(Tenure = factor(Tenure, levels = c("Renter", "Owner")))
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000, fill = Tenure)) +
geom_col(alpha = 0.7, color = "black") +
scale_fill_manual(name = NULL, values = c("steelblue", "darkgreen")) +
theme_agData(legend.position = "bottom") +
labs(title = "Housing Units in Canada",
y = "Million Units", x = NULL, caption = myCaption)
ggsave("canada_housing_3_03.png", mp, width = 6, height = 4)